1. Field of the Invention
The present invention relates generally to signal processing techniques and to image processing in particular. More precisely, the present invention relates to enhancement techniques for sharpening a signal whether or not it contains artifacts resulting from partial loss of information, e.g. through compression, in which case artifacts of compression like blocking and ringing are suppressed simultaneously with sharpening by supplementing or amplifying the high spatial frequencies of an image or, more generally, the high frequency component of any other type of a signal.
2. Discussion of the Related Art
It has been found that, due to the particular properties of the human visual system, perception of spatial image features is improved when the image appears sharper as compared to a blurred image, even if the amount of inherent visual information is the same in both images. A more detailed description of those findings can be found in Anderson, et al., U.S. Pat. No. 6,005,983 and references therein. Motivated by this fact, much effort has been made to provide methods of image sharpening suitable for various applications. Many methods may be successfully applied to enhance images to nearly perfect quality. On the other hand, rapid growth of the amount of digital images stored in the various electronic databases requires images be compressed in order to save memory space. Most compression methods, e.g. those used in the popular JPEG compression software, introduce their own artifacts that diminish the visual quality of stored images by varying degrees depending on the compression rate. However, even minor degradation of image quality may preclude the possibility of sharpening by an ad hoc method, as it would typically result in degrading an image even more. While several techniques of sharpening images and/or other types of signals have been formulated, the sharpening of images in the presence of, say, compression artifacts, e.g. blocking or ringing, has proved elusive. Some partial success in this direction has been achieved in: R. Coifinan, A. Sowa, “New methods of controlled total variation reduction for digital functions”, SIAM Journal on Numerical Analysis, VOL. 39, NO. 2 (2001), 480–498, which shows how to de-block a JPEG compressed image in such a way that it can be followed by sharpening when the blocking effect is not too strong. The aforementioned method, however, does not allow simultaneous reduction of the ringing artifact and is generally less natural and less successful, although characteristically nonlinear. Another partly successful approach was constructed by B. R. Frieden (B. R. Frieden, “A new algorithm for the preferential enhancement of edge gradients”, J. Opt. Soc. Am., 66 (1976), 280–283). This far-seeing approach combined the median and the Fourier filtering techniques in a rather direct way. However, it lacked the correct iterative flow-type formulation, which resulted in losses of the informational content of images. From the point of view of its applicability to tasks that are considered within the present invention, it also lacked other mechanisms of control that would be necessary for the method to be useful in the presence of compression artifacts.
There is a clear reason for that essential lack of previous solutions and the key issue is that most ad hoc engineering solutions rely too heavily, if not solely, on linear methods. Although almost all image enhancement techniques require an application of some mildly nonlinear operations, e.g. application of thresholds, (adaptive) quantization, rescaling in both the physical and the frequency domains, they tend to rely on the various linear, or at least short-time/small-scale linearizable, techniques at their core. Within the linear framework, one achieves sharpening by an application of operators or operations that are unbounded, which means that they will unavoidably result in magnification of discontinuities and errors, e.g. errors of interpolation and/or quantization.
On the other hand, many of the evolutionary nonlinear techniques based on partial-differential-equations techniques also fail when applied to the task at hand. In order to explain what actually happens to an image during the processing, one needs to explain what type of regularity is being restored or imposed on an image during the process. Typically for most methods a function is selected defining the regularity of an image, and then the resulting Euler-Lagrange equation is studied as a basis for constructing the regularizing flow, e.g., an iterative process. The nature of such algorithms is geometric in the sense that the function involves an integral of some function of first order partial derivatives, so that the resulting flow depends on some combination of second order derivatives of the evolving image. Because the flow needs to be defined independently of the choice of coordinates, a schema must be used that diminishes one measure of curvature or another. It should be noted that there can only be a limited number of flows of this type that would be essentially distinct from one another from the viewpoint of their utilization in some engineering task.